def sub_sfms(ax_M, galaxies, clrs, markers):
"""
Generate a SF vs. M* plot on top of PRIMUS galaxies
Args:
ax_M (matplotlib.axis):
galaxies (list):
List of FRB.galaxies.frbgalaxy.FRBGalaxy objects
clrs (list):
List of matplotlib colors
markers (list):
List of matplotlib marker types
"""
utils.set_mplrc()
# Load up
primus_zcat = Table.read(
os.path.join(primus_path, 'PRIMUS_2013_zcat_v1.fits.gz'))
primus_mass = Table.read(
os.path.join(primus_path, 'PRIMUS_2014_mass_v1.fits.gz'))
gdz = (primus_zcat['Z'] > 0.2) & (primus_zcat['Z'] < 0.4)
gd_mag = primus_zcat['SDSS_ABSMAG'][:, 0] != 0.
good_mass = primus_mass['ISGOOD'] == 1
# PRIMUS
# Photometry
gd_color = gdz & gd_mag
u_r = primus_zcat['SDSS_ABSMAG'][gd_color,
0] - primus_zcat['SDSS_ABSMAG'][gd_color,
2]
rmag = primus_zcat['SDSS_ABSMAG'][gd_color, 2]
# Mass/SFR
gd_msfr = good_mass & gdz
mass = primus_mass['MASS'][gd_msfr]
sfr = primus_mass['SFR'][gd_msfr]
# Plot
ms = 22.
# Histogram
xbins = 50
ybins = 50
counts, xedges, yedges = np.histogram2d(mass, sfr, bins=(xbins, ybins))
#cm = plt.get_cmap('Reds')
cm = plt.get_cmap('Greys')
# SF
mplt = ax_M.pcolormesh(xedges, yedges, counts.transpose(), cmap=cm)
# Relation
logm_star = np.linspace(8, 12)
logsfr = lambda logm_star: -0.49 + 0.65 * (logm_star - 10) + 1.07 * (0.35 -
0.1)
ax_M.plot(logm_star, logsfr(logm_star), "k--",
lw=3) #, label="Moustakas et al 2013")
# Galaxies
for kk, galaxy in enumerate(galaxies):
# M*
if 'Mstar' in galaxy.derived.keys():
logM, sig_logM = utils.log_me(galaxy.derived['Mstar'],
galaxy.derived['Mstar_err'])
elif 'Mstar_spec' in galaxy.derived.keys():
logM, sig_logM = np.log10(galaxy.derived['Mstar_spec']), 0.3
else:
continue
# SFR
if 'SFR_nebular_err' in galaxy.derived.keys():
logS, sig_logS = utils.log_me(galaxy.derived['SFR_nebular'],
galaxy.derived['SFR_nebular_err'])
else:
logS, sig_logS = utils.log_me(galaxy.derived['SFR_nebular'],
0.3 * galaxy.derived['SFR_nebular'])
# Plot
ax_M.errorbar([logM], [logS],
xerr=sig_logM,
yerr=sig_logS,
color=clrs[kk],
marker=markers[kk],
markersize="12",
capsize=3,
label=galaxy.name)
if sig_logS is None:
# Down arrow
plt.arrow(logM,
logS,
0.,
-0.2,
fc=clrs[kk],
ec=clrs[kk],
head_width=0.02 * 4,
head_length=0.05 * 2)
ax_M.annotate(r"\textbf{Star forming}", (8.5, 0.8), fontsize=13.)
ax_M.annotate(r"\textbf{Quiescent}", (11, -0.9), fontsize=13.)
ax_M.set_xlabel("$\log \, (M_*/M_\odot)$")
ax_M.set_ylabel("$\log \, SFR (M_\odot$/yr)")
ax_M.legend(loc='lower left')
ax_M.set_xlim(7.5, 11.8)
ax_M.set_ylim(-2.5, 1.2)
def sub_bpt(ax_BPT,
galaxies,
clrs,
markers,
show_kewley=True,
SDSS_clr='BuGn',
show_legend=True,
bptdat=None):
"""
Generate a BPT diagram
To use this code, you must download the SDSS_BPT_stellar_mass.fits file from
https://drive.google.com/open?id=1yHlfsvcRPXK73F6hboT1nM4bRF59ESab
and put it in data/Public/SDSS
Args:
ax_BPT (matplotlib.Axis):
galaxies (list):
List of FRBGalaxy objects
clrs (list):
List of colors
markers (list):
List of markers
show_kewley (bool, optional):
Show the BPT lines?
SDSS_clr (str, optional):
Set the color map for SDSS
show_legend (bool, optional):
Show a legend
bptdat (Table like):
SDSS BPT data
Returns:
ax_BPT is modified in place
"""
# Read in data
if bptdat is None:
sdss_file = os.path.join(resource_filename('frb', 'data'), 'Public',
'SDSS', 'SDSS_BPT_stellar_mass.fits')
if not os.path.isfile(sdss_file):
print("See the method notes to download the SDSS data!")
return
hdulist = fits.open(sdss_file)
bptdat = hdulist[1].data
# Select only non zero entries and SNR over 5
lines = np.array(bptdat.names)[[("flux" in name) & ("err" not in name)
for name in bptdat.names]]
line_err = np.array(
bptdat.names)[["flux_err" in name for name in bptdat.names]]
select = {}
for line, err in zip(lines, line_err):
select[line] = bptdat[line] / bptdat[err] >= 5
# SDSS
bpt1 = select['oiii_5007_flux'] & select['h_beta_flux'] & select[
'nii_6584_flux'] & select['h_alpha_flux']
y = bptdat['oiii_5007_flux'][bpt1] / bptdat['h_beta_flux'][bpt1]
x = bptdat['nii_6584_flux'][bpt1] / bptdat['h_alpha_flux'][bpt1]
xbins = 100
ybins = 100
# Plot
counts, xedges, yedges = np.histogram2d(np.log10(x),
np.log10(y),
bins=(xbins, ybins))
cm = plt.get_cmap(SDSS_clr)
mplt = ax_BPT.pcolormesh(xedges,
yedges,
np.log10(counts.transpose()),
cmap=cm)
# Loop on the Galaxies
for kk, galaxy in enumerate(galaxies):
# Parse the emission lines
NII, NII_err = galaxy.calc_nebular_lum('[NII] 6584')
Ha, Ha_err = galaxy.calc_nebular_lum('Halpha')
Hb, Hb_err = galaxy.calc_nebular_lum('Hbeta')
try:
OIII, OIII_err = galaxy.calc_nebular_lum('[OIII] 5007')
except:
import pdb
pdb.set_trace()
#
x0 = (NII / Ha).decompose().value
y0 = (OIII / Hb).decompose().value
x0_err = x0 * np.sqrt((NII_err / NII).decompose().value**2 +
(Ha_err / Ha).decompose().value**2)
y0_err = y0 * np.sqrt((OIII_err / OIII).decompose().value**2 +
(Hb_err / Hb).decompose().value**2)
# Require at least 20% error
x0_err = max(x0_err, 0.2 * x0)
y0_err = max(y0_err, 0.2 * y0)
logx, xerr = utils.log_me(x0, x0_err)
logy, yerr = utils.log_me(y0, y0_err)
# Upper limit on [NII]/Ha?
if NII_err.value < 0.:
xerr = None
# Left arrow
plt.arrow(logx,
logy,
-0.05,
0.,
fc=clrs[kk],
ec=clrs[kk],
head_width=0.02,
head_length=0.05)
# Plot
ax_BPT.errorbar([logx], [logy],
xerr=xerr,
yerr=yerr,
color=clrs[kk],
marker=markers[kk],
markersize="8",
capsize=3,
label=galaxy.name)
# Standard curves
demarc = lambda x: 0.61 / (
x - 0.05
) + 1.3 # Kauffman et al 2003, MNRAS, 346, 4, pp. 1055-1077. Eq 1
demarc_kewley = lambda x: 0.61 / (
x - 0.47
) + 1.19 # Kewley F., Dopita M., Sutherland R., Heisler C., Trevena J., 2001, ApJ, 556,121
demarc_liner = lambda x: 1.01 * x + 0.48 # Cid Fernandes et al 2010, MNRAS, 403,1036 Eq 10
ax_BPT.plot(np.linspace(-2, 0), demarc(np.linspace(-2, 0)), "k-",
lw=2) #, label="Kauffman et al 2003")
if show_kewley:
ax_BPT.plot(np.linspace(-2, 0.25),
demarc_kewley(np.linspace(-2, 0.25)),
"k--",
lw=2) #, label="Kewley et al 2001")
ax_BPT.plot(np.linspace(-0.43, 0.5),
demarc_liner(np.linspace(-0.43, 0.5)),
"k--",
lw=2) #, label="Cid Fernandes et al 2010")
# Labels
lsz = 13.
ax_BPT.annotate(r"\textbf{Star-forming}", (-1.30, 0), fontsize=lsz)
ax_BPT.annotate(r"\textbf{LINER}", (0.23, 0), fontsize=lsz)
ax_BPT.annotate(r"\textbf{Seyfert}", (-0.5, 1), fontsize=lsz)
# Legend
if show_legend:
ax_BPT.legend(loc="lower left")
# Axes
ax_BPT.set_xlabel(r"$\log \, ({\rm [N\textsc{ii}]/H\,\alpha)}$")
ax_BPT.set_ylabel(r"$\log \, ({\rm [O\textsc{iii}]/H\,\beta)}$")
ax_BPT.set_xlim(-1.5, 0.5)
ax_BPT.set_ylim(-1, 1.2)
utils.set_fontsize(ax_BPT, 13.)
